Camera-equipped devices are ubiquitous and proliferating day-to-day life. Accurate heart rate (HR) estimation from face videos acquired from low cost cameras in a non-contact manner can be used in many real-world scenarios and hence require rigorous exploration. HR estimation using face videos is based on the phenomenon that color and motion variations in the face videos are closely related to heart beat. The variations also contain noise due to facial expressions, respiration, eye blinking and environmental factors. Temporal signals depicting motion or color variations in frames across time are estimated from Region of Interest (ROI) using Eulerian or Lagrangian approaches. In a Lagrangian approach, temporal signals are determined by explicitly tracking the ROI or discriminating features over time. Such tracking is computationally expensive hence usually temporal signals are estimated using Eulerian approach, i.e., temporal signals are obtained by fixing the ROI and analyzing its variations. The Eulerian approach however works accurately for small variations.